# AI use cases for AI Engineering Course (amitshekhariitbhu/ai-engineering-course) Repo: [amitshekhariitbhu/ai-engineering-course](https://github.com/amitshekhariitbhu/ai-engineering-course) | Use cases: 4 | Page: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/ A free, step-by-step AI Engineering course in Markdown, with 18 modules and 146+ lessons from machine learning basics through LLMs, RAG, agents, inference, evaluation, safety, and system design. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Map a step-by-step learning path into AI engineering: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/plan-llm-engineer-learning-path/ - Prepare for AI engineer interviews with course material: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/prepare-ai-engineer-interview/ - Compare LLM serving tools before picking one: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/choose-llm-serving-stack/ - Design a RAG pipeline from the retrieval lessons: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/study-rag-pipeline-design/ ## How to map a step-by-step learning path into AI engineering with AI Engineering Course Repo: [amitshekhariitbhu/ai-engineering-course](https://github.com/amitshekhariitbhu/ai-engineering-course) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README Turn the 18-module course into a dated reading plan you can follow in order. **Who it is for:** A software engineer who wants to move into AI engineering and does not know which topics to study first. **The problem:** The field covers many topics, and it is hard to tell what to learn before what. **The outcome:** A written plan that lists each module in the README order, with the lessons to read in each one. ### Steps 1. Open https://github.com/amitshekhariitbhu/ai-engineering-course and read the Table of Contents. 2. Read the 'How to Use This AI Engineering Course' section and note the rule to follow modules in order. 3. Read the 'Curriculum at a Glance' table and record the lesson count for each module. 4. Write a plan that schedules Module 0 and Module 1 first, then the remaining modules in sequence. 5. Mark the modules that match your goal, such as RAG (Module 9) or inference (Module 12), and add them to the plan. ### Prompt to paste into your agent ```text Using the README of the repo amitshekhariitbhu/ai-engineering-course, build a module-by-module study plan for . Output a numbered list of modules in the README order with the lesson count for each, and check that Module 1 and Module 2 appear before any later module. ``` ### Caveat The course is a set of blog-style lessons in the README, so the plan depends on reading those lessons and does not install or run any code from the repo. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/plan-llm-engineer-learning-path/ ## How to prepare for AI engineer interviews with course material with AI Engineering Course Repo: [amitshekhariitbhu/ai-engineering-course](https://github.com/amitshekhariitbhu/ai-engineering-course) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README Use the interview module, glossary, and FAQs to drill core AI engineering terms. **Who it is for:** A candidate with aScheduled AI engineer, LLM engineer, or ML engineer interview who needs a fast review of key concepts. **The problem:** Interview questions cover many terms across training, RAG, agents, and inference, and it is hard to review them all. **The outcome:** A short list of concepts to explain from memory, each linked to the lesson that covers it. ### Steps 1. Open the repo at https://github.com/amitshekhariitbhu/ai-engineering-course and go to Module 18: Prepare for AI Engineering Interviews. 2. Read the AI Engineering Key Concepts Glossary section. 3. Read the AI Engineering Course FAQs section. 4. Pick five terms from the glossary that match the job you are applying for, such as RAG, KV cache, or LoRA. 5. Explain each term in your own words, then open the matching module to check your explanation. ### Prompt to paste into your agent ```text Read the README of amitshekhariitbhu/ai-engineering-course, take the terms in the Key Concepts Glossary, and create a quiz of 10 questions for . Output each question with the module it comes from, and verify that every answer points to a section that exists in the README. ``` ### Caveat The README lists interview preparation as a module and a glossary, but the actual question content lives in the linked lessons, which you should open to confirm. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/prepare-ai-engineer-interview/ ## How to compare LLM serving tools before picking one with AI Engineering Course Repo: [amitshekhariitbhu/ai-engineering-course](https://github.com/amitshekhariitbhu/ai-engineering-course) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Use the inference module to compare the serving tools the README names. **Who it is for:** An engineer deciding how to serve an LLM and who needs a map of the options before testing any of them. **The problem:** Serving tools differ in batching, memory use, and hardware support, and the choice is hard to make without a framework for comparing them. **The outcome:** A comparison note that lists each serving tool named in the README with the inference concepts it relates to. ### Steps 1. Open Module 12: LLM Inference Engineering in the repo README. 2. Read the list of topics: prefill vs decode, KV cache, paged attention, continuous batching, and quantization. 3. List the tools named in the course: llama.cpp, Ollama, vLLM, SGLang, and TensorRT-LLM. 4. For each tool, write which of the inference topics it is linked to in the course. 5. Open the lesson for each topic and check the claims before you use the note in a decision. ### Prompt to paste into your agent ```text From the README of amitshekhariitbhu/ai-engineering-course, extract the inference topics in Module 12 and the tools it names, then build a table for . Output the table with topic, tool, and the lesson name, and verify that every tool appears in the README text. ``` ### Caveat The README names these tools but does not compare their performance, so the table only shows which topics the course links to each tool and does not rank them. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/choose-llm-serving-stack/ ## How to design a RAG pipeline from the retrieval lessons with AI Engineering Course Repo: [amitshekhariitbhu/ai-engineering-course](https://github.com/amitshekhariitbhu/ai-engineering-course) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Use the RAG module to list the retrieval options before you build a pipeline. **Who it is for:** A developer who has been asked to add search over company documents to an LLM app and needs to choose retrieval methods. **The problem:** RAG has many options such as chunking, hybrid search, rerankers, and query rewriting, and it is unclear which ones to use. **The outcome:** A design note that lists the RAG components to consider, with the lesson that covers each one. ### Steps 1. Open Module 9: Vector Search and Retrieval-Augmented Generation (RAG) in the README. 2. List the topics: vector databases, ANN search, semantic search, hybrid search, rerankers, and chunking. 3. List the advanced topics: Contextual Retrieval, HyDE, query rewriting, multi-query retrieval, and GraphRAG. 4. Mark which topics fit your document set and your latency budget. 5. Write the design note with one line for each chosen topic and the lesson to read before you build it. ### Prompt to paste into your agent ```text Read Module 9 in the README of amitshekhariitbhu/ai-engineering-course and draft a RAG design note for . Output a list of chosen retrieval components with the matching lesson names, and verify that each component appears in the Module 9 topic list. ``` ### Caveat The README lists RAG topics but does not include code or a build guide, so you still need to write and test the pipeline with other tools. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/amitshekhariitbhu-ai-engineering-course/study-rag-pipeline-design/